2025/02/19 by Francesco Crocetti, Alberto Dionigi, Crocetti, Francesco +7 · 1 citation
Computer Science · Neuroscience · #Advanced Vision and Imaging #Computer Graphics and Visualization Techniques #FOS: Computer and information sciences #Robotics (cs.RO) #Visual perception and processing mechanisms
paper · pdf · doi:10.48550/arxiv.2502.13708
openalex publication_date 2025/02/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Visual Odometry (VO) and Visual SLAM (V-SLAM) systems often struggle in low-light and dark environments due to the lack of robust visual features. In this paper, we propose a novel active illumination framework to enhance the performance of VO and V-SLAM algorithms in these challenging conditions. The developed approach dynamically controls a moving light source to illuminate highly textured areas, thereby improving feature extraction and tracking. Specifically, a detector block, which incorporates a deep learning-based enhancing network, identifies regions with relevant features. Then, a pan-tilt controller is responsible for guiding the light beam toward these areas, so that to provide information-rich images to the ego-motion estimation algorithm. Experimental results on a real robotic platform demonstrate the effectiveness of the proposed method, showing a reduction in the pose estimation error up to 75% with respect to a traditional fixed lighting technique.